Current microRNA (miRNA) study presumes miRNAs interact with the 3′UTR of mRNAs to inhibit translation. Because of the non-perfect complementary sequences between mRNA and miRNA, miRNA target determination constitutes a bottleneck in miRNA applications, even with the latest achievements in miRNA profiling of cancer signatures. Currently, only a few miRNA of the thousands known to exist in animals and viruses have had their functionality and targets experimentally verified. The known interaction between the 5′ end region of miRNA with the 3′ UTR of mRNA has been utilized by several programs for target prediction. However, because the stretch of perfectly-matched sequences can be as small as 6-mer and G-U wobbles can contribute to functional hybridization, the problem of false positive targets becomes a major issue.
MiRNAs are small non-coding RNAs which regulate gene expression either by direct cleavage of the target mRNA or by inhibition of protein synthesis while preserving the target mRNAs. In animals, nascent miRNA transcripts are processed into ˜70 nucleotide (nt) precursors (pre-miRNA) in the nucleus and exported into the cytoplasm to be cleaved by Dicer enzymes to generate ˜22 nt imperfect double stranded RNA (dsRNA). In most cases, one of the two strands, called mature miRNA, is incorporated into the RNA-induced silencing complex (RISC), while the other strand appears to be degraded. This mature miRNA-RISC complex interacts with mRNA by complementary sequences. The complex either cleaves the target mRNA when the miRNA and mRNA are almost totally complementary, or represses protein translation when there are only partial complementary sequences between them. In addition to post-transcriptional regulation, miRNA appears to influence DNA methylation.
The first miRNA lin-4 was found in C. elegans in 1993, and the second miRNA let-7 was found much later in 2000 (both were discovered through genetic mutation studies). The identification of let-7 raised the possibility of similar small RNAs, and hundreds of miRNAs have since been identified in plants, worms, vertebrates, and human viruses by a combination of computational predictions and reverse genetics. Currently 321 human miRNAs are listed in the miRBase::Sequences database at the Sanger Institute; a recent study has added 89 miRNAs (S. Griffiths-Jones, The microRNA Registry. Nucleic Acids Res 32: D109-11 (2004); I. Bentwich, A. Avniel, Y. Karov, R. Aharonov, S. Gilad, O. Barad, A. Barzilai, P. Einat, U. Einav, E. Meiri, E. Sharon, Y. Spector, and Z. Bentwich, Identification of hundreds of conserved and nonconserved human microRNAs. Nat Genet (2005)). Current estimates are that miRNAs represent ˜1% of each organism's genes, and show developmental stage-, cell type-, and tissue-specificity. Some miRNAs are highly abundant. Recent comparative analysis of the human, mouse, rat and dog genome suggests that there are more miRNAs to be identified and that known miRNAs regulate at least 20% of human genes.
Studies of RNA interference, where dsRNAs are processed into ˜21 nt lengths and one strand's perfect complement to mRNA guides the RISC to degrade mRNAs, have progressed rapidly, and the shared biochemical processes of miRNA are rather well known. Publications on miRNAs are also quickly accumulating, including recent genome-wide miRNA profiling efforts and other data. This may leave the impression that miRNA function is relatively well characterized. However, fewer than 10 miRNAs in animal have experimentally validated functions and targets (Table 1). Unlike plant miRNAs, whose complementary sequences very closely match the target mRNA, most animal miRNAs are only partially complementary, making it a daunting task to connect miRNA-mRNA functional pairs. To complicate matters, one miRNA can target several genes, while one gene may be regulated by multiple miRNAs.
TABLE 1Experimentally-verified miRNA functions and targetsTargetRefer-AnimalmiRNAGeneFunctionencesCaenorhabditislin-4lin-14developmental timing [9, 19]eleganslin-28developmental timing[20]let-7lin-41developmental timing[21]hbl-1developmental timing[22, 23]lsy-6cog-1neuronal cell fate[24]miR-273die-1neuronal cell fate[25]DrosophilabantamHidcell death,[26]melanogasterproliferationmiR-14Unknowncell death, fat[27]storageMus musculusmiR-181aUnknownhaematopoietic[28]cell fatemiR-196Hoxb8unknown (direct[29]cleavage)miR-375Mtpninsulin secretion[30](isletspecific)Homo sapiensmiR-143ERK5adipocyte differ-[31]entiationmiR-84RASunknown (cancer[32](let-7related)family)miR-17-5pE2F1tumor suppressor?[33]miR-20aE2F1tumor suppressor?[33]
Several computational efforts to identify miRNA targets have been well covered in a recent review (J. R. Brown and P. Sanseau, A computational view of microRNAs and their targets. Drug Discov Today 10: 595-601 (2005)). All of these algorithms are based on the knowledge of two known miRNAs (lin-4 and let-7) and their target mRNAs: partial complementary sequences to the target mRNA 3′UTR (untranslated region in 3′-end side) and conserved target 3′ UTR sequences in orthologous genes. Even before the experimental confirmation of critical pairing of the 5′ end region of miRNAs, this feature was widely included in all the prediction tools following a computational approach. In the first versions of TargetS can (B. P. Lewis, I. H. Shih, M. W. Jones-Rhoades, D. P. Bartel, and C. B. Burge, Prediction of mammalian microRNA targets. Cell 115: 787-98 (2003)) and miRanda (A. J. Enright, B. John, U. Gaul, T. Tuschl, C. Sander, and D. S. Marks, MicroRNA targets in Drosophila. Genome Biol 5: R1 (2003)), multiple binding sites in a 3′ UTR were predicted, while RNAhybrid (M. Rehmsmeier, P. Steffen, M. Hochsmann, and R. Giegerich, Fast and effective prediction of microRNA/target duplexes. Rna 10: 1507-17 (2004)) and the second version of TargetScanS (B. P. Lewis, C. B. Burge, and D. P. Bartel, Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120: 15-20 (2005)) include a single binding site per UTR. Recently, an algorithm to identify targets for both single miRNAs and combinations of miRNAs has been developed, showing coordinated miRNA control (A. Krek, D. Grun, M. N. Poy, R. Wolf, L. Rosenberg, E. J. Epstein, P. MacMenamin, I. da Piedade, K. C. Gunsalus, M. Stoffel, and N. Rajewsky, Combinatorial microRNA target predictions. Nat Genet 37: 495-500 (2005)). Some of the computationally predicted targets were confirmed by a reporter gene assay containing target 3′ UTR sequences. Single-site target prediction have improved by structure-function studies of a model miRNA (M. Kiriakidou, P. T. Nelson, A. Kouranov, P. Fitziev, C. Bouyioukos, Z. Mourelatos, and A. Hatzigeorgiou, A combined computational-experimental approach predicts human microRNA targets. Genes Dev 18: 1165-78 (2004)), while secondary structures of target 3′ UTRs were considered in an algorithm (H. Robins, Y. Li, and R. W. Padgett, Incorporating structure to predict microRNA targets. Proc Natl Acad Sci USA 102: 4006-9 (2005)). The importance of architecture of 3′ UTR target sites has been experimentally demonstrated.
Unfortunately, predicted targets for mammalian miRNAs lack overlap among research groups. Recent progress in miRNA detection methods via microarrays enables the detection of genome-wide miRNA expression patterns. One study tried to correlate miRNA and mRNA co-expression with previously predicted miRNAs and target mRNAs pairs, but reported unrelated expression patterns and predicted targets, raising doubts regarding the validity of target mRNAs. Additionally, recent rigorous experiments show the levels of putative target mRNAs computationally predicted with verified reporter assays to be independent of the tested miRNA levels. These studies raise the possibility of mRNA degradation or translational level regulation by miRNAs. Currently, miR-196 is the only known animal miRNA that cleaves mRNA directly through its almost total complementary sequence to the target mRNA (only one G:U wobble among 21 nt), a rare feature in animal miRNAs. Another study reported that a broad range of mRNAs showed reduced expression levels due to externally introduced miRNAs. The study also reported that most of the reduced mRNAs had partially matched sequences in their 3′ UTR with the introduced miRNA. Therefore, even given the current progress, there is a major lack of knowledge of the function and targets of miRNAs.
A recent NEWS & VIEW section in the journal Nature reported on three cancer-related miRNA studies (P. S. Meltzer, Cancer genomics: small RNAs with big impacts. Nature 435: 745-6 (2005)): a global miRNA profile study of various tumor types (J. Lu, G. Getz, E. A. Miska, E. Alvarez-Saavedra, J. Lamb, D. Peck, A. Sweet-Cordero, B. L. Ebert, R. H. Mak, A. A. Ferrando, J. R. Downing, T. Jacks, H. R. Horvitz, and T. R. Golub, MicroRNA expression profiles classify human cancers. Nature 435: 834-8 (2005)), and two studies focused more on the miRNA cluster from a c13orf25 gene (a gene among amplified copies in a chromosome 13 fragment in human lymphomas). Different miRNA profiles of normal and cancer cells have been reported previously, but the new global miRNA profile study showed, remarkably, that the expression pattern of miRNAs can define cancer types better than mRNA expression. The other two studies represent the complexity of the miRNA-related regulation process, building on the reports that miRNA genes themselves are frequently located at cancer related genomic regions. One group nominated c13orf25 miRNA as a candidate non-coding oncogene, while the other group proposed that the miRNAs encoded from the c13orf25 gene may antagonize the effects of different oncogenes.
Accordingly, there is a need for accurate miRNA target prediction models. Specifically, there is a great need for a model that predicts targets based not only on the 3′UTR interactions but also on 5′UTR interactions (e.g., bridging action).